A Methodology for Processing Problem Constraints in Genetic Programming
Created by W.Langdon from
gp-bibliography.bib Revision:1.7954
- @Article{janikow:1996:CGP,
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author = "Cezary Z. Janikow",
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title = "A Methodology for Processing Problem Constraints in
Genetic Programming",
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journal = "Computers and Mathematics with Applications",
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year = "1996",
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volume = "32",
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number = "8",
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pages = "97--113",
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month = oct,
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keywords = "genetic algorithms, genetic programming, lil-gp",
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URL = "http://www.cs.umsl.edu/~janikow/psdocs/cgp.CMwA.ps",
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broken = "http://www.sciencedirect.com/science/article/B6TYJ-41GX54B-9/1/5a1e263b86597ce90a6eec429c357ce5",
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URL = "http://citeseer.ist.psu.edu/326996.html",
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ISSN = "0898-1221",
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DOI = "doi:10.1016/0898-1221(96)00170-8",
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size = "17 pages",
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abstract = "Search mechanisms of artificial intelligence combine
two elements: representation, which determines the
search space, and a search mechanism, which actually
explores the space. Unfortunately, many searches may
explore redundant and/or invalid solutions. Genetic
programming refers to a class of evolutionary
algorithms based on genetic algorithms, but using a
parameterized representation in the form of trees.
These algorithms perform searches based on simulation
of nature. They face the same problems of
redundant/invalid subspaces. These problems have just
recently been addressed in a systematic manner. This
paper presents a methodology devised for the public
domain genetic programming tool lil-gp. This
methodology uses data typing and semantic information
to constrain the representation space so that only
valid, and possibly unique, solutions will be explored.
The user enters problem-specific constraints, which are
transformed into a normal set. This set is checked for
feasibility, and subsequently, it is used to limit the
space being explored. The constraints can determine
valid, possibly unique spaces. Moreover, they can also
be used to exclude subspaces the user considers
uninteresting, using some problem-specific knowledge. A
simple example is followed thoroughly to illustrate the
constraint language, transformations, and the normal
set. Experiments with Boolean 11-multiplexer illustrate
practical applications of the method to limit redundant
space exploration by using problem-specific
knowledge.",
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notes = "http://laplace.cs.umsl.edu/~janikow/cgp-lilgp/ CGP
uses GP [Koza] to evolve programs (or trees in
general). It extends GP by allowing syntactic and
sematical constraints on function calls (the
constraints can be weighted rather than strict), plus
function overloading. In future releases, evolution of
representation (i.e., constraints), ADFs, and recursive
functions are planned.
lil-gp comparison of solving 11-multiplexor problem
nine different ways with different type systems. Some
tighter (than Koza) type systems (eg different address
and data bits, different function sets) are worse than
Koza GP and some are better. Problem dependant reasons
for this suggested. Comparison with GIL. STGP",
- }
Genetic Programming entries for
Cezary Z Janikow
Citations